Automatic Speech Recognition
NeMo
ONNX
GGUF
parakeet
tdt
sherpa-onnx
multilingual
speech-recognition
gabor
fastconformer
Instructions to use aoiandroid/orukeet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- NeMo
How to use aoiandroid/orukeet with NeMo:
import nemo.collections.asr as nemo_asr asr_model = nemo_asr.models.ASRModel.from_pretrained("aoiandroid/orukeet") transcriptions = asr_model.transcribe(["file.wav"]) - Notebooks
- Google Colab
- Kaggle
| """Use an installation receipt without downloading anything during transcription.""" | |
| import argparse | |
| import json | |
| import sys | |
| from pathlib import Path | |
| from orukeet import Orukeet | |
| def main(): | |
| if hasattr(sys.stdout, "reconfigure"): | |
| sys.stdout.reconfigure(encoding="utf-8") | |
| parser = argparse.ArgumentParser(description=__doc__) | |
| parser.add_argument('audio', type=Path, nargs='+') | |
| parser.add_argument('--installation', type=Path, default=Path('installation.json')) | |
| args = parser.parse_args() | |
| config = json.loads(args.installation.read_text(encoding='utf-8-sig')) | |
| with Orukeet(config['model'], config['runtime'], device=config['device']) as model: | |
| for path in args.audio: | |
| print(json.dumps({'file': str(path), **model.transcribe(path)}, ensure_ascii=False)) | |
| if __name__ == '__main__': | |
| main() | |